“Theories crumble, but good observations never fade.”—Harlow Shapley

These quotes wafted into mind after reading John Cadogan’s criticism of Kent Hawkins’ thesis about using a comprehensive system accounting for the relevant thermal variables involved in “integrating” wind technology’s highly variable intermittent flutter.

There are at least two issues inherent in Cadogan’s comment. He’s perfectly correct about the desirability of getting Hawkins’ work into a first rate energy journal, which I trust will happen soon. On the issue of the substance of Hawkins’ ideas as he exposes the slipshod methods of Milligan, et al, Hawkins’ argument would be correct even if he presented it on papyrus to the Washington Press Corps.

The energy pundits from the National Renewable Energy Lab are hardly engaged in dispassionate work, for their jobs and career futures are in thrall to the success of technologies like industrial wind. Any magician knows how easily it is to fool people who have a stake in the outcome of an illusion.

And any scientist, in contrast to pundits, understands the necessity of eliminating bias in the conduct of experiment. Science insists upon conclusions that account for all the evidence, not selective pieces which fit the convenience of a particular point of view. A given experiment, however honestly and intelligently conducted, can yield the “wrong” answer for a variety of reasons. This is why experiments must be checked by other scientists, using other instruments, other conditions, even other ideas.

Which brings me back to Harlow Shapley’s quip about the overriding importance of good observation–as the final arbiter of successful hypotheses. The Myth authors are very good at using Authority, statistical calumny (as Hawkins demonstrates), and abstract “projections” to bolster their arguments. But they assiduously avoid measuring real-world behavior at meaningful time increments to account for all factors, in ways consistent with the dictates of the First Law of Thermodynamics.

Who is really fooling whom? The idea that a wholly random, unreliable, always fluctuating, non-dispatchable energy source controllable only through curtailment can be incorporated without significant thermal consequences into a system driven by the quest for utter reliability and control is, in my view, delusional.

Perhaps Mr. Cardogan might agree that genuine real-time measurement is required, in the form of sustained chronological load dispatch analyses at 15-minutes intervals, closely examining the heat rate penalties involved as wind sporadically displaces existing power sources, and its continuous ebb and flow is in turn balanced by reliable thermal generators. If so, perhaps he’ll join me in urging an independent, dispassionate evaluation of wind performance–and not simply accept a government report that tailors both methods and conclusions to fit the needs of its client. And then submits them for review by those who also have a stake in the outcome. At the very least, he might consider insisting that wind subsidies be indexed to actual measured reductions of CO2 throughout a grid system.

Admittedly, since wind performance data is typically proprietarily “confidential,” actual measurement might be difficult to do. People like Shapley would be appalled, for how can one get good observations when the data is intentionally hidden from view? Which makes it easier for NREL pundits to pull a Dragon in My Garage stunt as they attempt to bamboozle the public into believing their pigs can fly.

The assault on science by those who “Believe in the Wind” (do recall the Vestas ad campaign) is profound. Good for Kent Hawkins for asking people to think about industrial wind technology in a meaningful scientific way, even though they might only want to clap their hands to help the industry survive its life and death struggle against unbelief.

Try submiiting your paper to IEEE for peer review, rather than an anti-wind site.

Don’t believe what’s written here folks.

Jack Cadogan

That same E.ON Netz report revealed sizable weather forecasting errors for wind energy—ranging from -370 MW to + 477 MW on 6250 MW of installed capacity, and that, during individual hours, the error was as large as +/-2900 MW. Furthermore, when reviewing the results of a timed study of eight-hour-ahead wind production forecasts over 6650 MW of installed wind capacity from 7,000 widely dispersed wind turbines, E.ON Netz found that, although forecasting errors of more than 1000 MW were “fairly rare,” about one-third of the time they exceeded 500 MW. This casts doubt on persistent claims that better forecasting of wind availability will allow grid operators sufficient knowledge to accurately plan the amount of backup generation necessary to safely compensate for the widest wind fluctuations. Such evidence reinforces the axiom that wind patterns are inherently random. Moreover, the size of the E.ON Netz sample demonstrates that spreading large numbers of wind turbines across the landscape—diversifying the aggregate wind supply–would not vastly improve the stochastic likelihood of more accurate forecasting, as many wind developers maintain.

Subsequent analysis by Britain’s Jim Oswalt confirms this finding.

Kent Hawkins in this commentary effectively demolishes the “methods” used by wind boosters to bolster self-serving claims that wind volatility can be functionally tamed through widely-spaced, coordinated wind projects. Stochastics don’t work very well when applied to making such wind projects that much more predictable. Utter randomness can only spawn more randomness. What a wind project does at one time, at one place, typically has little bearing on what another wind project might do a hundred miles away. Or what the same project might do at a different time. And the history of one day–or one hour–has no bearing on how the wind might behave at any future time.

With about 35,000 industrial wind machines now in operation across North America, most of them widely-scattered, there should be plenty of opportunity to investigate whether stochastically refined wind behavior can achieve a more meaningful capacity credit beyond what a stopped clock can achieve on an hourly basis (8.3%).

Hawkins’ call for long-term, finely grained chronological load dispatch analyses should be an imperative for those interested in the application of disinterested science–or those simply interested in making an industry accountable for its claims.

But wait…! The wind industry’s actual performance data is enshrouded behind proprietary confidentiality. How, uh, convenient.